New realistic IoT network intrusion dataset (MU-IoT) with comprehensive attack scenarios for cybersecurity research. Published in IEEE 2024 with 4+ citations. Covers multiple IoT protocols and device types.
Dataset with 500 controlled simulation scenarios analyzing ICSHSO-based dynamic optimization for energy efficiency in wireless sensor networks. Includes network lifetime, PDR, residual energy, and transmission reduction metrics.
Comprehensive dataset capturing cybersecurity threats and sustainability metrics in smart city IoT and edge networks, including communication behavior, energy consumption patterns, and attack scenarios.
500 simulation scenarios analyzing dynamic optimization techniques for energy efficiency in IoT sensor networks. Includes network lifetime, PDR, and energy consumption metrics.
1,000 records of simulated IoT network activity with blockchain-based security. Covers DDoS, malware, MITM attacks across device, network, and application layers.
Real-time IoT sensor data collected from industrial machines for predictive maintenance and anomaly detection in smart manufacturing environments, featuring temperature, vibration, pressure readings, and machine operational status for Industry 4.0 applications.
Comprehensive smart home dataset generated using OpenSHS simulator with 29 IoT sensors monitoring daily activities across multiple rooms, including labeled data for eating, sleeping, working, and anomaly detection in residential environments.
Real-time environmental dataset from IoT-enabled smart homes focusing on energy consumption optimization and occupant comfort, with 15-minute interval readings of temperature, humidity, lighting, air quality, CO2 levels, and HVAC control data.
Comprehensive energy consumption dataset from a smart home with detailed weather information, containing readings from 17 different appliances and devices with 13 weather parameters for advanced consumption forecasting.
IoT sensor data from smart building systems for energy management. Contains real-time power consumption, HVAC operations, lighting control, and appliance-level energy disaggregation data.
Simulated data for analyzing the stability of smart grids under the Decentral Smart Grid Control (DSGC) concept. Classifies grid state as stable or unstable.